Open Access Open Access  Restricted Access Subscription or Fee Access

Multimodal Medical Image Fusion Under Redundant Transforms


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v10i3.4888

Abstract


Multimodal medical image fusion gives the complementary information present in each imaging sensor combined into a single image. This is done to reduce the data, better visualization and clinical diagnosis. In this paper, an efficient fusion approach based on redundant transforms, which, in particular, restores and shift-invariant the image as fused image contains artifacts when reconstructed. Different wavelets like Daubechies, orthogonal, Biorthogonal, Symlets and Curvelets are compared with the proposed technique. Medical image fusion results are determined using subjective and objective analysis with the existing state of the art techniques which include wavelets and curvelets. The comparative analysis of the obtained fusion results are performed with RMS error, Entropy, Correlation coefficient, PSNR, Standard deviation, Spatial Frequency and Median frequency. The combined subjective and objective evaluation of the proposed fusion method shows the accuracy and increased robustness.
Copyright © 2015 Praise Worthy Prize - All rights reserved.

Keywords


Medical Image Fusion; Redundant Transforms; Wavelets; Curvelets

Full Text:

PDF


References


V. Barra and J.Y. Boire, “A general framework for the fusion of anatomical and functional medical images,” Neuro Image, vol. 13, no. 3, pp. 410–424, 2001.
http://dx.doi.org/10.1006/nimg.2000.0707

B. V. Dasarathy, “Information fusion in the realm of medical applications a bibliographic glimpse at its growing appeal,” Information Fusion, vol. 13, no. 1, pp. 1–9, 2012.
http://dx.doi.org/10.1016/j.inffus.2011.06.003

O. Rockinger and T. Fechner, “Pixel-level image fusion: the case of image sequences,” in Signal Processing, Sensor Fusion, and Target Recognition VII, vol. 3374 of Proceedings of SPIE, pp. 378– 388, April 1998.
http://dx.doi.org/10.1117/12.327135

C. Pohl and J. L. Van Genderen, “Multisensor image fusion in remote sensing: concepts, methods and applications,” International Journal of Remote Sensing, vol. 19, no. 5, pp. 823–854, 1998.
http://dx.doi.org/10.1080/014311698215748

V. Petrovic, Multisensor Pixel-level image fusion [Ph.D. dissertation], University of Manchester, 2001.

J. J. Lewis, R. J. O’Callaghan, S. G. Nikolov, D. R. Bull, and C. N. Canagarajah, “Region-based image fusion using complex wavelets,” in Proceedings of the 7th International Conference on Information Fusion (FUSION ’04), International Society of Information Fusion (ISIF), Stockholm, Sweden, pp. 555–562, July 2004.
http://dx.doi.org/10.1016/j.inffus.2005.09.006

T. Stathaki, Image Fusion: Algorithms and Applications, Academic Press, 2011.

V. P. S. Naidu and J. R. Raol, “Pixel-level image fusion using wavelets and principal component analysis,” Defence Science Journal, vol. 58, no. 3, pp. 338–352, 2008.
http://dx.doi.org/10.14429/dsj.58.1653

J. G. P. W. Clevers and R. Zurita-Milla, “Multisensor and multiresolution image fusion using the linear mixing model,” Image Fusion: Algorithms and Applications, , pp. 67–84, Academic Press, Elsevier, 2008.
http://dx.doi.org/10.1016/b978-0-12-372529-5.00004-4

J. Tian, L. Chen, L. Ma, and W. Yu, “Multi-focus image fusion using a bilateral gradient-based sharpness criterion,” Optics Communications, vol. 284, no. 1, pp. 80–87, 2011.
http://dx.doi.org/10.1016/j.optcom.2010.08.085

A. B. Hamza, Y. He, H. Krim, and A. Willsky, “A multiscale approach to pixel-level image fusion,” Integrated Computer- Aided Engineering, vol. 12, no. 2, pp. 135–146, 2005.

H. Li, B. S. Manjunath, and S. K. Mitra, “Multisensor image fusion using the wavelet transform,” Graphical Models and Image Processing, vol. 57, no. 3, pp. 235–245, 1995.
http://dx.doi.org/10.1006/gmip.1995.1022

P. J. Burt and E. H. Adelson, “The laplacian pyramid as a compact image code,” IEEE Transactions on Communications, vol. 31, no. 4, pp. 532–540, 1983.
http://dx.doi.org/10.1109/tcom.1983.1095851

P. J. Burt, “A gradient pyramid basis for pattern selective image fusion,” in Proceedings of the SID International Symposium, pp.467–470, 1992.

A. Toet, L. J. van Ruyven, and J. M. Valeton, “Merging thermal and visual images by a contrast pyramid,” Optical Engineering, vol. 28, no. 7, pp. 789–792, 1989.
http://dx.doi.org/10.1117/12.7977034

A. Toet, “Image fusion by a ration of low-pass pyramid,” Pattern Recognition Letters, vol. 9, no. 4, pp. 245–253, 1989.
http://dx.doi.org/10.1016/0167-8655(89)90003-2

A. Toet, “A morphological pyramidal image decomposition,” Pattern Recognition Letters, vol. 9, no. 4, pp. 255–261, 1989.
http://dx.doi.org/10.1016/0167-8655(89)90004-4

C. H. Anderson, “Filter-subtract-decimate hierarchical pyramid signal analyzing and synthesizing technique,” US Patent 4718104 A, 1998.

H. Zhang, L. Liu, and N. Lin, “A novel wavelet medical image fusion method,” in Proceedings of the International Conference on Multimedia and Ubiquitous Engineering (MUE ’07), pp. 548– 553, April 2007.
http://dx.doi.org/10.1109/mue.2007.40

S. Vekkot and P. Shukla, “A novel architecture for wavelet based image fusion, ”World Academy of Science, Engineering and Technology, vol. 57, pp. 372–377, 2009.

Y. Yang, “Multiresolution image fusion based on wavelet transform by using a novel technique for selection coefficients,” Journal of Multimedia, vol. 6, no. 1, pp. 91–98, 2011.
http://dx.doi.org/10.4304/jmm.6.1.91-98

J. Teng, X. Wang, J. Zhang, S. Wang, and P. Huo, “A multimodality medical image fusion algorithm based on wavelet transform,” in Advances in Swarm Intelligence, vol. 6146 of Lecture Notes in Computer Science, pp. 627–633, Springer, Berlin, Germany, 2010.
http://dx.doi.org/10.1007/978-3-642-13498-2_82

Y. Yang, D. S. Park, S. Huang, and N. Rao, “Medical image fusion via an effective wavelet-based approach,” Eurasip Journal on Advances in Signal Processing, vol. 2010, Article ID 579341,2010.
http://dx.doi.org/10.1155/2010/579341

Y. Yang, “Multimodal medical image fusion through a new DWT based technique,” in Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE ’10), pp. 1–4, June 2010.
http://dx.doi.org/10.1109/icbbe.2010.5517037

B. Alfano, M. Ciampi, and G. D. Pietro, “A wavelet-based algorithm for multimodal medical image fusion,” in Semantic Multimedia, pp. 117–120, Springer, Berlin, Germany, 2007.
http://dx.doi.org/10.1007/978-3-540-77051-0_13

G. Qu, D. Zhang, and P. Yan, “Medical image fusion by wavelet transform modulus maxima,” Optics Express, vol. 9, no. 4, pp. 184–190, 2001.
http://dx.doi.org/10.1364/oe.9.000184

E.J. Candes, Ridgelets: Theory and applications. Ph.D. Dissertation, Department of Statistics, Stanford University, August 1998.

J.L. Starck, E. Candes, D. Donoho, The curvelets transform for image denoising, IEEE Trans. Image Processing 11 (6) (2002) 670–684.
http://dx.doi.org/10.1109/tip.2002.1014998

M.N. Do, M. Vetterli, The contourlet transform: an efficient directional multiresolution image representation, IEEE Trans. Image Processing 14 (12) (2005) 2091–2106.
http://dx.doi.org/10.1109/tip.2005.859376

R. Eckhorn, H.J. Reitboeck, M. Arndt, P.W. Dicke, A neural network for feature linking via synchronous activity: Results from cat visual cortex and from simulations, in: R.M.J. Cotterill (Ed.), Models of Brain Function, Cambridge Univ. Press, Cambridge, UK, 1989, pp. 255–272.
http://dx.doi.org/10.1109/ijcnn.1989.118659

R. Eckhorn, H.J. Reitboeck, M. Arndt, P.W. Dicke, Feature linking via synchronization among distributed assemblies: Simulation of results from cat cortex, Neural Computation 2 (1990) 293–307.
http://dx.doi.org/10.1162/neco.1990.2.3.293

H. S. Ranganath, G. Kuntimad, J.L. Johnson, Pulse coupled neural networks for image processing, in: Proc. 1995 IEEE Southeast Con, Raleigh, NC, 1995, pp. 37–43.
http://dx.doi.org/10.1109/secon.1995.513053

J. L. Johnson, H.S. Ranganath, G. Kuntimad, H.J. Caulfield, Pulse coupled neural networks, in: O. Omidvar, J. Dayhoff (Eds.), Neural Networks and Pattern Recognition, Academic, San Diego, CA, 1998, pp. 1–56.
http://dx.doi.org/10.1016/b978-012526420-4/50002-1

J.L. Johnson, D. Ritter, Observation of periodic waves in a pulse coupled neural network, Optics Letters 18 (1993) 1253.
http://dx.doi.org/10.1364/ol.18.001253

J.L. Johnson, M.L. Padgett, PCNN models and applications, IEEE Transactions on Neural Networks 10 (3) (1999).
http://dx.doi.org/10.1109/72.761706

T. Lindblad, J.M. Kinser, Image Processing Using Pulse-Coupled Neural Networks, second revised ed., Springer, 2005.
http://dx.doi.org/10.1007/978-1-4471-3617-0

Yide Ma, Lian Li, et al., Principle of Pulse-Coupled Neural Network and Its Applications, Science Press, Beijing, China, 2006.

Jianwen. Hu, Shutao. Li, The multiscale directional bilateral filter and its applications to multisensory image fusion, Information Fusion vol 13, issue 3, 2012, p.p 196-206.
http://dx.doi.org/10.1016/j.inffus.2011.01.002

R. Fattal, M. Agrawala, S. Rusinkiewicz, Multiscale shape and detail enhancement from multi-light image collections, ACM Transactions on Graphics 26 (3) (2007) 51-1–51-9.
http://dx.doi.org/10.1145/1276377.1276441

A. Ellmauthaler, Carla. L. Pagliari, Eduardo A. B. da Silva, “Multiscale Image Fusion Using the Undecimated Wavelet Transform With Spectral Factorization and Nonorthogonal Filter Banks”, IEEE Transactions on Image Processing, Vol. 22, NO. 3, March 2013, p.p 1005-1017.
http://dx.doi.org/10.1109/tip.2012.2226045

V. S. Petrovic and C. S. Xydeas, “Gradient-based multiresolution image fusion,” IEEE Trans. Image Process., vol. 13, no. 2, pp. 228–237, Feb.2004.
http://dx.doi.org/10.1109/tip.2004.823821

Bhatnagar, G., Jonathan Wu, Q. M., & Liu, Z. (2013). Human visual system inspired multimodal medical image fusion framework. Expert Systems with Applications, 40(5), 1708–1720.
http://dx.doi.org/10.1016/j.eswa.2012.09.011

Singh, R., & Khare, A. (2014). Fusion of multimodal medical images using Daubechies complex wavelet transform-A multiresolution approach. Information Fusion, 19, 49–60.
http://dx.doi.org/10.1016/j.inffus.2012.09.005

Serikawa, S., Lu, H.M., & Li, Y.J., (2013). Multimodal medical image fusion in extended contourlet transform domain. Springer: Software Engineering, Artificial Intelligence, Networking, pp. 215–226.
http://dx.doi.org/10.1007/978-3-642-32172-6_17

Barman Manisha, J. Choudhury Pal, A fuzzy rule base system for the diagnosisof heart disease, International Journal of Computer Applications 57 (7) (2012)46–53.

A. Kumbhar, A. Kulkarni, U. Sutar, Fusion of multiple features in mag-netic resonant image segmentation using genetic algorithm, in: IEEE 3rd International Conference on Advance Computing, 2013, pp. 819–825, http://dx.doi.org/10.1109 /IAdCC.2013.6514 332.
http://dx.doi.org/10.1109/iadcc.2013.6514332

Yang-Ping Wang, Jian-Wu Dang, Qiang Li, Sha Li, Multimodal medical imagefusion using fuzzy radial basis function neural networks, International Con-ference on Wavelet Analysis and Pattern Recognition 2 (2007) 778–782.
http://dx.doi.org/10.1109/icwapr.2007.4420774

Jiong-Hua Teng, Fusion algorithm of medical images based on fuzzy logic,International Conference on Fuzzy Systems and Knowledge Discovery 2 (2010)546–550.
http://dx.doi.org/10.1109/fskd.2010.5569561

C.T. Kavitha, C. Chellamuthu, R. Rajesh, Multimodal medical image fusion usingdiscrete ripplet transform and intersecting cortical model, Procedia Engineer-ing 38 (2012) 1409–1414.
http://dx.doi.org/10.1016/j.proeng.2012.06.175

Liyong Bao, Dongfeng Zhao, Dongming Zhou, Image fusion algorithm based on m-PCNN, in: Second International Workshop on Education Technology and Computer Science, 2010, pp. 235– 238.
http://dx.doi.org/10.1109/etcs.2010.95

C.T. Kavitha, C. Chellamuthu, R. Rajesh, Medical image fusion using com-bined discrete wavelet and ripplet transforms, Procedia Engineering 38 (2012)813–820.
http://dx.doi.org/10.1016/j.proeng.2012.06.102

C.T. Kavitha, C. Chellamuthu, R. Rajesh, Medical image fusion using integerwavelet transform and pulse-coupled neural network, in: IEEE InternationalConference on Computational Intelligence and Computing Research, 2011, pp.834–838, ISBN: 978-1-61284-694-1.

W.W. Kong, Y.J. Lei, Y. Lei, S. Lu, Image fusion technique based on non-subsampled contourlet transform and adaptive unit-fast-linking pulse-coupled neural network, IET Image Processing 5 (2) (2011) 113–121.
http://dx.doi.org/10.1049/iet-ipr.2009.0425

S. Erkanli, Z.U. Rahman, Entropy-based image fusion with continuousgenetic algorithm, in: 10th International Conference on Intelli-gent Systems Design and Applications (ISDA), 2010, pp. 278– 283.
http://dx.doi.org/10.1109/isda.2010.5687253

M. Bhattacharya, A. Das, Multimodality medical image registration andfusion techniques using mutual information and genetic algorithm basedapproaches, Advances in Experimental Biology and Medicine 696 (2011)441–449.
http://dx.doi.org/10.1007/978-1-4419-7046-6_44

S.H. Ling, H.T. Nguyen, Natural occurrence of nocturnal hypoglycemiadetection using hybrid particle swarm optimized fuzzy reasoning model, Artificial Intelligence in Medicine 55 (3) (2012) 177–184.
http://dx.doi.org/10.1016/j.artmed.2012.04.003

Mehdi Neshat, Mehdi Sargolzaei, Adel Nadjaran Toosi, Azra Masoumi,Hepatitis disease diagnosis using hybrid case based reasoning and particle swarm optimization, ISRN Artificial Intelligence 2012 (2012) 6 pages.
http://dx.doi.org/10.5402/2012/609718

S.H. Ling, H.T. Nguyen, Genetic-algorithm-based multiple regression with fuzzyinference system for detection of nocturnal hypoglycemic episodes, IEEE Trans-actions on Information Technology in Biomedicine 15 (2) (2011) 308–315.
http://dx.doi.org/10.1109/titb.2010.2103953

Theodor Heinze, Robert Wierschke, Alexander Schacht, Martin von Löwis, Ahybrid artificial intelligence system for assistance in remote monitoring of Heart patients, in: Corchado Emilio, Marek Kurzynski, Michal Wozniak (Eds.),Hybrid Artificial Intelligent Systems, 6679, LNCS, 2011, pp. 413–420.
http://dx.doi.org/10.1007/978-3-642-21222-2_50

Candès, E. J. and Donoho, D. L. (2004), New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities. Comm. Pure Appl. Math., 57: 219–266. doi: 10.1002/cpa.10116.
http://dx.doi.org/10.1002/cpa.10116

M. Antonini, M. Barlaud, P. Mathieu and I. Daubechies, “ Image coding using wavelet transform”, IEEE Transactions Image processing, Vol 1, pp. 205-220, 1992.
http://dx.doi.org/10.1109/83.136597


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize